Driving the discovery, development, and commercialization of groundbreaking therapies through innovative science.
Our mission is to eliminate inaccuracy and waste when translating drug candidates to clinical trials using AI-driven, multi-disciplinary quantitative methods that predict patient outcomes.
What if?
Introducing a first-in-class decision engine that enables AI drug discovery and R&D de-risking, unlike anything you’ve worked with before.
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BIOiSIM-accelerated assets in pipeline
Our AI-driven drug development services deliver insights for pharmaceutical companies, biotechs, and academic & research institutions. We also partner in risk-sharing collaborations to develop promising drugs.
PulmoSIM Therapeutics, a wholly-owned brand of VeriSIM Life, identifies licensable candidates for preclinical translation and out-licensing to commercialization partners.
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Making a difference
Addressing the unmet needs of patients everywhere is our passion. Are you motivated by ownership in your work and a mission to make a difference? Do you embrace diversity? Is continuous learning part of your DNA? This is what drives the team at VeriSIM Life every day. Learn more about our mission and make up.
News and events
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Introducing AtlasGEN™ Novel Drug Designer
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
